Load in the libraries, set working directory, and load in the data

My working directory is already set but I like to manually set it each time to make sure there are no errors.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.1
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
setwd("C:/Users/andre/OneDrive/Documents/School/Data 110")
nations <- read_csv("nations.csv")
## Rows: 5275 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): iso2c, iso3c, country, region, income
## dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(nations)
## # A tibble: 6 × 10
##   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
##   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
## 1 AD    AND   Andorra  1996         NA      64291       10.9                2.8
## 2 AD    AND   Andorra  1994         NA      62707       10.9                3.2
## 3 AD    AND   Andorra  2003         NA      74783       10.3                2  
## 4 AD    AND   Andorra  1990         NA      54511       11.9                4.3
## 5 AD    AND   Andorra  2009         NA      85474        9.9                1.7
## 6 AD    AND   Andorra  2011         NA      82326       NA                  1.6
## # ℹ 2 more variables: region <chr>, income <chr>

Create a new variable in the data set

Added the variable “gdp” to the data set.

nations <- nations %>%
  mutate(gdp = (gdp_percap * population) / 1e12) 

head(nations)
## # A tibble: 6 × 11
##   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
##   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
## 1 AD    AND   Andorra  1996         NA      64291       10.9                2.8
## 2 AD    AND   Andorra  1994         NA      62707       10.9                3.2
## 3 AD    AND   Andorra  2003         NA      74783       10.3                2  
## 4 AD    AND   Andorra  1990         NA      54511       11.9                4.3
## 5 AD    AND   Andorra  2009         NA      85474        9.9                1.7
## 6 AD    AND   Andorra  2011         NA      82326       NA                  1.6
## # ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>

Create the new data sets and check the first

Looks good. Also create a new variable “gdp2” which is just a cleaned version of “gdp” without NA values.

nations1 <- filter(nations, country %in% c("United States", "China", "Japan", "Germany"))
nations2 <- nations %>%
  group_by(region, year) %>%
  summarise(GDP = sum(gdp, na.rm = TRUE))
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
head(nations1)
## # A tibble: 6 × 11
##   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
##   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
## 1 CN    CHN   China    1992      1260. 1164970000       18.3               29.4
## 2 CN    CHN   China    2005      5053. 1303720000       12.4               14  
## 3 CN    CHN   China    2000      2915. 1262645000       14.0               21.2
## 4 CN    CHN   China    1991      1091. 1150780000       19.7               29.7
## 5 CN    CHN   China    2013     12219. 1357380000       12.1                6.3
## 6 CN    CHN   China    1999      2650. 1252735000       14.6               22.2
## # ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>

Check the second new data set

Looks good.

head(nations2)
## # A tibble: 6 × 3
## # Groups:   region [1]
##   region               year   GDP
##   <chr>               <dbl> <dbl>
## 1 East Asia & Pacific  1990  5.52
## 2 East Asia & Pacific  1991  6.03
## 3 East Asia & Pacific  1992  6.50
## 4 East Asia & Pacific  1993  7.04
## 5 East Asia & Pacific  1994  7.64
## 6 East Asia & Pacific  1995  8.29

Create the first plot

Using “nations1” I create the first plot.

p1 <- ggplot(nations1, aes(x = year, y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(title = "China's Rise to Become the Largest Economy",
       x = "Year",
       y = "GDP (trillions of dollars)") +
  guides(color = guide_legend(title = "Country"))
p1

Attempt at making the plot interactive

I was able to make it interactive with ease but can not get it to display “population”. I tried mutating the variable to see if the legnth of the numbers were the issue but that didn’t change anything.

p1_active <- ggplotly(p1, tooltip = c("country", "gdp"))
p1_active

Create the second plot

Using “nations2” I create the second plot. Making sure to use “fill” in the aes function and “color” in the geom_area function.

p2 <- ggplot(nations2, aes(x = year, y = GDP, fill = region)) +
  geom_area(color = "white")+
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP by World Bank Region",
       x = "Year",
       y = "GDP (trillions of dollars)") +
  guides(fill = guide_legend(title = "Region"))
p2

Attempt at making it interactive

I am having a hard time getting it so when you scroll over the region it displays the data rather than being on the line.

p2_active <- ggplotly(p2, tooltip = c("y", "x", "region"))
p2_active